AI Engineer(NLP)

We're on the lookout for some awesome new additions to our team here at SrinSoft, and if you think you might just be the perfect fit then do drop us an email at tms@srinsofttech.com

AI Engineer(NLP)

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• *Location:** Chennai, Hyderabad and Pune

• *Experience:** 4 to 7 Years

• *Job Description:**

• Design and implement NLP solutions (classification, extraction, summarization, Q\&A) aligned to product goals.
• Build Retrieval\-Augmented Generation (RAG) pipelines: indexing, chunking, embedding strategies, and reranking.
• Develop multimodal workflows when required (text \+ images/structured data) and define interfaces to downstream services.
• Fine\-tune or adapt models (where appropriate) and establish evaluation datasets and metrics.
• Must have worked with MCP based architecture.
• Optimize inference for latency, throughput, and cost: batching, caching, quantization, and model selection strategies.
• Implement robust prompt and tool orchestration with guardrails, monitoring, and fallbacks.
• Collaborate with MLOps to productionize training/inference, including experiment tracking and model registry usage.
• Write clean, testable code; review PRs and mentor other engineers on ML best practices.
• Create technical docs for system design, model choices, and operational runbooks.

• *Required Qualifications:**

• Strong experience building NLP systems using PyTorch and Transformer\-based architectures.
• Hands\-on experience implementing RAG pipelines end\-to\-end (retrieval, reranking, generation, evaluation).
• Solid understanding of LLM behavior, prompting patterns, and failure modes (hallucination, tool misuse, leakage).
• Experience with Git\-based development, Docker, and CI/CD practices for ML services.
• Working knowledge of relational and non\-relational databases and how they fit into AI products.
• Ability to design APIs/inference services that are reliable and maintainable

• *Preferred Qualifications**

• Experience with multimodal models and document understanding pipelines.
• Experience with Kubernetes\-based deployments and scaling strategies.
• Knowledge of Azure ML and cloud\-native patterns for production AI systems.
• Experience with model distillation/quantization and serving optimization.
• Prior experience mentoring or leading a small technical workstream.

• *Core Tools \& Technologies:**

• PyTorch, Transformers; optional TensorFlow familiarity
• RAG components: embeddings, vector stores, rerankers, eval harnesses
• Docker; CI/CD; Git
• Datastores: SQL \+ NoSQL; optional vector databases
• Kubernetes; Azure compute \& services (where applicable)